


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE			: Tuple				   =					8



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=BITS					)       ->		Optional[Any]:
    _lowercase			:				Dict 						=						x.device

    _lowercase			:				Tuple 						=						(x * 255).int().clamp(0				,					255					)

    _lowercase			:				int 						=						2 ** torch.arange(bits - 1				,					-1				,					-1				,					device=SCREAMING_SNAKE_CASE__					)
    _lowercase			:				Dict 						=						rearrange(SCREAMING_SNAKE_CASE__				,					'd -> d 1 1'					)
    _lowercase			:				Any 						=						rearrange(SCREAMING_SNAKE_CASE__				,					'b c h w -> b c 1 h w'					)

    _lowercase			:				Tuple 						=						((x & mask) != 0).float()
    _lowercase			:				Optional[Any] 						=						rearrange(SCREAMING_SNAKE_CASE__				,					'b c d h w -> b (c d) h w'					)
    _lowercase			:				List[Any] 						=						bits * 2 - 1
    return bits



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=BITS					)       ->		Union[str, Any]:
    _lowercase			:				Tuple 						=						x.device

    _lowercase			:				int 						=						(x > 0).int()
    _lowercase			:				Tuple 						=						2 ** torch.arange(bits - 1				,					-1				,					-1				,					device=SCREAMING_SNAKE_CASE__				,					dtype=torch.intaa					)

    _lowercase			:				Tuple 						=						rearrange(SCREAMING_SNAKE_CASE__				,					'd -> d 1 1'					)
    _lowercase			:				str 						=						rearrange(SCREAMING_SNAKE_CASE__				,					'b (c d) h w -> b c d h w'				,					d=8					)
    _lowercase			:				Optional[Any] 						=						reduce(x * mask				,					'b c d h w -> b c h w'				,					'sum'					)
    return (dec / 255).clamp(0.0				,					1.0					)



def 							UpperCamelCase_( self				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_ = 0.0				,					lowerCamelCase_ = True				,					lowerCamelCase_=None				,					lowerCamelCase_ = True				,					)       ->		Optional[int]:





    if self.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'					)

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    _lowercase			:				Any 						=						timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    _lowercase			:				Optional[int] 						=						self.alphas_cumprod[timestep]
    _lowercase			:				List[Any] 						=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    _lowercase			:				Union[str, Any] 						=						1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    _lowercase			:				Union[str, Any] 						=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    _lowercase			:				Any 						=						self.bit_scale
    if self.config.clip_sample:
        _lowercase			:				Optional[int] 						=						torch.clamp(SCREAMING_SNAKE_CASE__				,					-scale				,					SCREAMING_SNAKE_CASE__					)

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    _lowercase			:				List[Any] 						=						self._get_variance(SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__					)
    _lowercase			:				str 						=						eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        _lowercase			:				List[Any] 						=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    _lowercase			:				List[str] 						=						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    _lowercase			:				str 						=						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        _lowercase			:				List[str] 						=						model_output.device if torch.is_tensor(SCREAMING_SNAKE_CASE__					) else """cpu"""
        _lowercase			:				Optional[int] 						=						torch.randn(model_output.shape				,					dtype=model_output.dtype				,					generator=SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
        _lowercase			:				List[Any] 						=						self._get_variance(SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__					) ** 0.5 * eta * noise

        _lowercase			:				int 						=						prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__				,					pred_original_sample=SCREAMING_SNAKE_CASE__					)



def 							UpperCamelCase_( self				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_="epsilon"				,					lowerCamelCase_=None				,					lowerCamelCase_ = True				,					)       ->		Optional[Any]:
    _lowercase			:				int 						=						timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        _lowercase			:				Union[str, Any] 						=						torch.split(SCREAMING_SNAKE_CASE__				,					sample.shape[1]				,					dim=1					)
    else:
        _lowercase			:				Any 						=						None

    # 1. compute alphas, betas
    _lowercase			:				Union[str, Any] 						=						self.alphas_cumprod[t]
    _lowercase			:				Any 						=						self.alphas_cumprod[t - 1] if t > 0 else self.one
    _lowercase			:				Any 						=						1 - alpha_prod_t
    _lowercase			:				List[str] 						=						1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        _lowercase			:				List[Any] 						=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        _lowercase			:				Optional[Any] 						=						model_output
    else:
        raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''					)

    # 3. Clip "predicted x_0"
    _lowercase			:				List[str] 						=						self.bit_scale
    if self.config.clip_sample:
        _lowercase			:				str 						=						torch.clamp(SCREAMING_SNAKE_CASE__				,					-scale				,					SCREAMING_SNAKE_CASE__					)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    _lowercase			:				Dict 						=						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    _lowercase			:				Union[str, Any] 						=						self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    _lowercase			:				Union[str, Any] 						=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    _lowercase			:				Optional[int] 						=						0
    if t > 0:
        _lowercase			:				Optional[int] 						=						torch.randn(
            model_output.size()				,					dtype=model_output.dtype				,					layout=model_output.layout				,					generator=SCREAMING_SNAKE_CASE__					).to(model_output.device					)
        _lowercase			:				Tuple 						=						(self._get_variance(SCREAMING_SNAKE_CASE__				,					predicted_variance=SCREAMING_SNAKE_CASE__					) ** 0.5) * noise

    _lowercase			:				int 						=						pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__				,					pred_original_sample=SCREAMING_SNAKE_CASE__					)






class _lowerCamelCase(		_snake_case  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 1.0, )			->  Any:




           """simple docstring"""

           super().__init__()
           _lowercase			:				str 						=						bit_scale
           _lowercase			:				List[Any] 						=						(
               ddim_bit_scheduler_step if isinstance(__snake_case, __snake_case) else ddpm_bit_scheduler_step
           )

           self.register_modules(unet=__snake_case, scheduler=__snake_case)




       @torch.no_grad()
       def __call__(   self, lowerCamelCase = 2_56, lowerCamelCase = 2_56, lowerCamelCase = 50, lowerCamelCase = None, lowerCamelCase = 1, lowerCamelCase = "pil", lowerCamelCase = True, **lowerCamelCase, )			->  Dict:




           """simple docstring"""

           _lowercase			:				List[str] 						=						torch.randn(
               (batch_size, self.unet.config.in_channels, height, width), generator=__snake_case, )
           _lowercase			:				Tuple 						=						decimal_to_bits(__snake_case) * self.bit_scale
           _lowercase			:				Optional[Any] 						=						latents.to(self.device)

           self.scheduler.set_timesteps(__snake_case)

           for t in self.progress_bar(self.scheduler.timesteps):
               # predict the noise residual
               _lowercase			:				Any 						=						self.unet(__snake_case, __snake_case).sample

               # compute the previous noisy sample x_t -> x_t-1
               _lowercase			:				Union[str, Any] 						=						self.scheduler.step(__snake_case, __snake_case, __snake_case).prev_sample

           _lowercase			:				str 						=						bits_to_decimal(__snake_case)

           if output_type == "pil":
               _lowercase			:				Optional[int] 						=						self.numpy_to_pil(__snake_case)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__snake_case)






'''simple docstring'''


import argparse
import datetime







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= {
				    """0""": """Sunday""",
				    """1""": """Monday""",
				    """2""": """Tuesday""",
				    """3""": """Wednesday""",
				    """4""": """Thursday""",
				    """5""": """Friday""",
				    """6""": """Saturday""",
				}

				_SCREAMING_SNAKE_CASE					:			Optional[int]							= {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

				# Validate
				if not 0 < len(SCREAMING_SNAKE_CASE__	) < 11:
								raise ValueError("""Must be 10 characters long"""	)

				# Get month
				_SCREAMING_SNAKE_CASE					:			int							= int(date_input[0] + date_input[1]	)
				# Validate
				if not 0 < m < 13:
								raise ValueError("""Month must be between 1 - 12"""	)

				_SCREAMING_SNAKE_CASE					:			str							= date_input[2]
				# Validate
				if sep_a not in ["-", "/"]:
								raise ValueError("""Date separator must be '-' or '/'"""	)

				# Get day
				_SCREAMING_SNAKE_CASE					:			int							= int(date_input[3] + date_input[4]	)
				# Validate
				if not 0 < d < 32:
								raise ValueError("""Date must be between 1 - 31"""	)

				# Get second separator
				_SCREAMING_SNAKE_CASE					:			str							= date_input[5]
				# Validate
				if sep_a not in ["-", "/"]:
								raise ValueError("""Date separator must be '-' or '/'"""	)

				# Get year
				_SCREAMING_SNAKE_CASE					:			int							= int(date_input[6] + date_input[7] + date_input[8] + date_input[9]	)
				# Arbitrary year range
				if not 45 < y < 8500:
								raise ValueError(
								    """Year out of range. There has to be some sort of limit...right?"""	)

				# Get datetime obj for validation
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= datetime.date(int(SCREAMING_SNAKE_CASE__	)				, int(SCREAMING_SNAKE_CASE__	)				, int(SCREAMING_SNAKE_CASE__	)	)

				# Start math
				if m <= 2:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= y - 1
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= m + 12
				# maths var
				_SCREAMING_SNAKE_CASE					:			int							= int(str(SCREAMING_SNAKE_CASE__	)[:2]	)
				_SCREAMING_SNAKE_CASE					:			int							= int(str(SCREAMING_SNAKE_CASE__	)[2:]	)
				_SCREAMING_SNAKE_CASE					:			int							= int(2.6 * m - 5.3_9	)
				_SCREAMING_SNAKE_CASE					:			int							= int(c / 4	)
				_SCREAMING_SNAKE_CASE					:			int							= int(k / 4	)
				_SCREAMING_SNAKE_CASE					:			int							= int(d + k	)
				_SCREAMING_SNAKE_CASE					:			int							= int(t + u + v + x	)
				_SCREAMING_SNAKE_CASE					:			int							= int(z - (2 * c)	)
				_SCREAMING_SNAKE_CASE					:			int							= round(w % 7	)
				# End math

				# Validate math
				if f != convert_datetime_days[dt_ck.weekday()]:
								raise AssertionError("""The date was evaluated incorrectly. Contact developer."""	)

				# Response
				_SCREAMING_SNAKE_CASE					:			str							= f"""Your date {date_input}, is a {days[str(SCREAMING_SNAKE_CASE__	)]}!"""
				return response


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				UpperCAmelCase_						:  Tuple				  =						argparse.ArgumentParser(
				    description=(
				        'Find out what day of the week nearly any date is or was. Enter '
				        'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
				    )
				)
				parser.add_argument(
				    'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
				)
				UpperCAmelCase_						:  List[Any]				  =						parser.parse_args()
				zeller(args.date_input)




